InfoExtractor 2.0 is a relation extraction baseline system developed for DuIE 2.0.
InfoExtractor 2.0 is a relation extraction baseline system developed for DuIE 2.0.
Different from [DuIE 1.0](http://lic2019.ccf.org.cn/kg), the new 2.0 task is more inclined to colloquial language, and further introduces **complex relations** which entails multiple objects in one single SPO.
Different from [DuIE 1.0](http://lic2019.ccf.org.cn/kg), the new 2.0 task is more inclined to colloquial language, and further introduces **complex relations** which entails multiple objects in one single SPO.
For detailed information about the dataset, please refer to the official website of our [competition](http://bjyz-ai.epc.baidu.com/aistudio/competition/detail/34?isFromCcf=true).
For detailed information about the dataset, please refer to the official website of our competition.
InfoExtractor 2.0 is built upon a SOTA pre-trained language model [ERNIE](https://arxiv.org/abs/1904.09223) using PaddlePaddle.
InfoExtractor 2.0 is built upon a SOTA pre-trained language model [ERNIE](https://arxiv.org/abs/1904.09223) using PaddlePaddle.
We design a structured **tagging strategy** to directly fine-tune ERNIE, through which multiple, overlapped SPOs can be extracted in **a single pass**.
We design a structured **tagging strategy** to directly fine-tune ERNIE, through which multiple, overlapped SPOs can be extracted in **a single pass**.
The InfoExtractor 2.0 system is simple yet effective, achieving 0.554 F1 on the DuIE 2.0 demo data and 0.848 F1 on DuIE 1.0.
The hyperparameters are simply set to: BATCH_SIZE=16, LEARNING_RATE=2e-5, and EPOCH=10 (without tuning).
### Tagging Strategy
### Tagging Strategy
Our tagging strategy is designed to discover multiple, overlapped SPOs in the DuIE 2.0 task.
Our tagging strategy is designed to discover multiple, overlapped SPOs in the DuIE 2.0 task.
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The code is tested on a single P40 GPU, with CUDA version=10.1, GPU Driver Version = 418.39.
The code is tested on a single P40 GPU, with CUDA version=10.1, GPU Driver Version = 418.39.
### Download Dataset
### Download Dataset
Please download the training data, development data from [the competition website](http://bjyz-ai.epc.baidu.com/aistudio/competition/detail/34?isFromCcf=true), then unzip files into `./data/` and rename them to `train.json`, `dev.json`.
Please download the training data, development data from the competition website, then unzip files into `./data/` and rename them to `train.json`, `dev.json`.
### Download pre-trained ERNIE model
### Download pre-trained ERNIE model
Download ERNIE1.0 Base（max-len-512）model and extract it into `./pretrained_model/`
Download ERNIE1.0 Base（max-len-512）model and extract it into `./pretrained_model/`
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### Official Evaluation
### Official Evaluation
Zip your prediction json file and then run official evaluation:
Zip your prediction json file and then run official evaluation:
```
```
zip ./data/predict_test.json.zip ./data/predict_test.json
zip ./data/predict_dev.json.zip ./data/predict_dev.json
Precision, Recall and F1 scores are used as the official evaluation metrics to measure the performance of participating systems. Alias file lists entities with more than one correct mentions. It is not provided due to security reasons.
Precision, Recall and F1 scores are used as the official evaluation metrics to measure the performance of participating systems. Alias file lists entities with more than one correct mentions. It is not provided due to security reasons.
